CRH2型动车组列车信息传输网络流量建模与预测

Modeling and Prediction on Train Communication Network Traffic of CRH2 EMUs

针对CRH2型动车组列车网络流量数据日益复杂的特性,提出了一种将主成分分析法(PCA)与后馈神经网络(BP网络)相结合的网络流量建模预测思路。基于已搭建好的CRH2型列车通信仿真平台,对该仿真网络各条链路进行流量采集。为了降低分析的复杂度,流量数据先进行PCA降维预处理分析,再将数据输入到BP神经预测网络模型进行仿真预测。经验证,该思路能有效拟合列车主体网络流量的变化趋势,为CRH2型动车组通信网络的故障诊断分析提供了一定的参考。

Aiming at the increasing complexity of the CRH2 train network traffic data,the method based on principal component analysis（PCA）and back propagation neural network（BP Network）was proposed to model and predict network traffic.Based on the built CRH2 train communication simulation platform,traffic of various links of the network has been collected.In order to reduce the complexity of analysis,the dimension reduction analysis is carried out with the application of PCA,then the data is input to BP network for simulation prediction.It is proved that the method can effectively fit the trend of the train network flow,providing concrete reference for the fault diagnosis of CRH2 train communication network.